Cho, Seung Woo, et al. "Investigating Temporal and Spatial Trends of Brand Images Using Twitter Opinion Mining." Information Science and Applications (ICISA), 2014 International Conference on. IEEE, 2014.

1. A Study on the Spacio-Temporal Trend
of Brand Index using Twitter Messages
Sentiment Analysis

2. Abstract
Twitter Data
Social
Scien
ce
Huma
n
ArtMedic
al
Econo
my
Sentiment
Analysis

3. Introduction
 Twitter Crawling
 Data Pre-processing
 Korean Morphology Analysis
 Twitter Opinion Mining
 Sentiment Dictionary
 Evaluating performance of candidate classifiers
 Sentiment Classification
 Visualize Associative Relationship of Terms
 Relationship with Brand Index

4. Twitter Crawling
Twitter API
Streaming API
REST API
- Search API
Get 1% of all
twitter data in
real time
Get twitter data
from the keyword
2013.9.9.Mon. 9:35pm ~ Now
About 10,000 ~ 15,000 tweets per a day
Total 1,220,000 tweets (2013.11.2.Sat)

5. Data Pre-Processing
 Only get tweets which contain at least more than 3 Korean characters and tweets within
a 500km radius of Seoul, Korea.
 To remove foreign languages, special characters
 Remove tweets which only contain location information.
 Remove retweets
‫ويتكلم‬ ‫نهائيا‬ ‫السمع‬ ‫فقد‬ ‫متعب‬ ‫ابو‬ ‫الملك‬ ‫ان‬ ‫خبر‬ ‫اكد‬ ‫المستوى‬ ‫رفيع‬ ‫وامير‬ ‫موثوق‬ ‫صدر‬
‫مفهوم‬ ‫وغير‬ ‫مترابط‬ ‫غير‬ ‫كالم‬((‫تخريف‬::)) Sat Oct 12 00:06:37 KST 2013
I'm at Club ELLUI - @ellui_seoul (서울특별시) w/ 2
others http://t.co/zhcrncosKH::Sat Oct 12 00:02:06 KST 2013

6. Korean Morpheme Analyzer
 꼬꼬마 Korean Morpheme Analyzer
 한나눔 Korean Morpheme Analyzer
 Komoran Korean Morpheme Analyzer
 Lucene Korean Analyzer
 은전한닢 Korean Morpheme Analyzer
 Performance of the analyzer
 Foreign language and slang tagging
 Sentiment related word tagging (slang,
verb, emoticon)
 It has good dictionary
 Don’t need to think about word spacing
 But, unable to perceive lots of emoticons,
metaphor, sarcasm, irony.

7. Korean Morpheme Analyzer
> 배가 아파서 병원에 갔다.
배 NN,F,배,*,*,*,*,*
가 JKS,F,가,*,*,*,*,*
아파서 VA+EC,F,아파서,Inflect,VA,EC,아프/VA+ㅏ서/EC,*
병원 NN,T,병원,*,*,*,*,*
에 JKB,F,에,*,*,*,*,*
갔 VV+EP,T,갔,Inflect,VV,EP,가/VV+ㅏㅆ/EP,*
다 EF,F,다,*,*,*,*,*
. SF,*,*,*,*,*,*,*
EOS
Noun
Verb
Adjective
Adverb
Root

8. Building Sentiment Dictionary
Manually labeled twitter data
1 • 6 days of twitter data (2013.9.9, 9.16, 9.23, 9.30, 10.7, 10.14)
• Labeled positive and negative sets of Noun, Adjective, Verb, Root (total 8 sets)
• Labeled by 4 person
2 • 20,000 reviews from 2 movies
• 545 positive set, 545 negative set,
545 neutral set
Naver Movie review data with rating
0
1000
2000
3000
4000
5000
6000
1 2 3 4 5 6 7 8 9 10
0
500
1000
1500
2000
2500
3000
3500
1 2 3 4 5 6 7 8 9 10
Positive
Positivenegative
Movie 1 Movie 2

9. Sentiment Classification
 SVM Classifier
 1. Training set - 150 positive set, 150 negative set (Twitter data)
2. Test set – 545 positive set, 545 negative set (Movie review data)
Accuracy = 70.64220183486239% (770/1090) (classification)
Mean squared error = 1.1743119266055047 (regression)
Squared correlation coefficient = 0.18400994471523438 (regression)
 Naïve bayes Classifier
 SO-PMI Classifier

10. Building Sentiment Dictionary
Unlabeled &
labeled data set
Ternary classifier : Naïve Bayes,
SO-PMI, SVM
Positive
set
Negative
set
Neutral
set
Positive
set
Negative
set
Neutral
set
Positive
set
Negative
set
Neutral
set
SO-PMI
SVM
Naïve Bayes

11. Sentiment of Brand Index
Samsung
Galaxy S2
Battery LCDPrice ….
: Brand (keyword)
: Related nouns (attribute)
Adjective
Verb
Noun
Adverb …
correlation
good
good nice
good good
Nice, pretty,
lovely …
Bad, terrible …
PMI(word, pword) + PMI(word, nword)
Determining
Objectivity

12. Scenario